Tobacco distribution based on improved K-means algorithm

机译：基于改进的K均值算法的烟草分布

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In order to solve the problem of distribution area segmentation of tobacco distribution, an improved k-means clustering algorithm was proposed in this paper. Firstly, the density of every node was calculated, and the first K nodes with the highest density were selected as initial clustering centers. Then the marginal nodes were prioritized to avoid the bad effect that marginal nodes might cause on clustering result. The experimental result demonstrated that the improved clustering algorithm not only avoided the local optima but also gave serious consideration to every important marginal node.

机译：为了解决烟草分布的分布区域分割问题，本文提出了一种改进的k均值聚类算法。首先，计算每个节点的密度，并且选择具有最高密度的第一k节点作为初始聚类中心。然后优先考虑边缘节点以避免边缘节点可能导致聚类结果的不良影响。实验结果表明，改进的聚类算法不仅避免了本地最佳算法，而且对每个重要的边缘节点进行了认真考虑。

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《IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics》|2009年||共5页
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中图分类 TP3-53;
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genetic algorithms; goods distribution; pattern clustering; tobacco industry; distribution area segmentation problem; k-means clustering algorithm; marginal node; tobacco distribution; K-means clustering; initial clustering center;

机译：遗传算法;商品分布;模式集群;烟草业;分布区分割问题;k均值聚类算法;边缘节点;烟草分布;k-merise集群;初始聚类中心;初始聚类中心;初始聚类中心;初始聚类中心;

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Tobacco distribution based on improved K-means algorithm

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